Metabolomics is the assessment of small molecules, often defined to be only those molecules participating in cellular metabolism, within a given biological system. Metabolites include low-molecular weight compounds, such as lipids, sugars, amino acids, nucleotides. Modern methods, such as Nuclear Magnetic Resonance (NMR) and Mass Spectroscopy (MS) coupled with liquid chromatography (LC) or gas chromatography (GC), can identify and quantify a large number of metabolites simultaneously within a biospecimen, capturing its metabolomic profile. These profiles have been used to predict the risk of diabetes, diagnose prostate cancer, and identify biomarkers of Crohn?s disease. There is a strong interest in applying metabolomic analysis to cancer epidemiologic studies. Since epidemiologic studies often involve field biospecimen collection, the conditions of sample processing, handling, and storage may vary, which may affect the reliability of the results and increase the risk of detecting false biomarker candidates. Therefore, to obtain an accurate assessment of an individual?s metabolomic profile, we need to understand and then eliminate or alter sample preparation steps that affect measured metabolite levels. At this stage, our interest is to understand whether collection methods (e.g. time until freezing) or sample preparation steps (e.g. metabolite extraction) affect the bias or variability of a measurement. It is important to realize the potential variability from the sample handling to properly control for the effect of pre-analytical parameters on metabolite measurements when performing metabolomics research. Understanding this variability is an essential prerequisite to broadening the use of metabolomics technologies to epidemiological studies and pooling measurements from different population-based studies.